arXiv subjects

Chunyang Chen

Publications and source records attributed to Chunyang Chen.

At least 91 records · Page 5Linked to original sources

arXiv2023

Psychologically-Inspired, Unsupervised Inference of Perceptual Groups of GUI Widgets from GUI Images

Graphical User Interface (GUI) is not merely a collection of individual and unrelated widgets, but rather partitions discrete widgets into groups by various visual cues, thus forming higher-order perceptual units such as tab, menu, card or list. The ability to automatically segment a GUI into perceptual groups of widgets constitutes a fundamental component of visual intelligence to automate GUI design, implementation and automation tasks. Although humans can partition a GUI into meaningful perceptual groups of widgets in a highly reliable way, perceptual grouping is still an open challenge for computational approaches. Existing methods rely on ad-hoc heuristics or supervised machine learning that is dependent on specific GUI implementations and runtime information. Research in psychology and biological vision has formulated a set of principles (i.e., Gestalt theory of perception) that describe how humans group elements in visual scenes based on visual cues like connectivity, similarity, proximity and continuity. These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasant and usability. Inspired by these principles, we present a novel unsupervised image-based method for inferring perceptual groups of GUI widgets. Our method requires only GUI pixel images, is independent of GUI implementation, and does not require any training data. The evaluation on a dataset of 1,091 GUIs collected from 772 mobile apps and 20 UI design mockups shows that our method significantly outperforms the state-of-the-art ad-hoc heuristics-based baseline. Our perceptual grouping method creates the opportunities for improving UI-related software engineering tasks.

cs.HC↗

arXiv2023

Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing

Mobile apps are indispensable for people's daily life, and automated GUI (Graphical User Interface) testing is widely used for app quality assurance. There is a growing interest in using learning-based techniques for automated GUI testing which aims at generating human-like actions and interactions. However, the limitations such as low testing coverage, weak generalization, and heavy reliance on training data, make an urgent need for a more effective approach to generate human-like actions to thoroughly test mobile apps. Inspired by the success of the Large Language Model (LLM), e.g., GPT-3 and ChatGPT, in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within it, we extract the static context of the GUI page and the dynamic context of the iterative testing process, design prompts for inputting this information to LLM, and develop a neural matching network to decode the LLM's output into actionable steps to execute the app. We evaluate GPTDroid on 86 apps from Google Play, and its activity coverage is 71%, with 32% higher than the best baseline, and can detect 36% more bugs with faster speed than the best baseline. GPTDroid also detects 48 new bugs on the Google Play with 25 of them being confirmed/fixed. We further summarize the capabilities of GPTDroid behind the superior performance, including semantic text input, compound action, long meaningful test trace, and test case prioritization.

cs.SE↗

arXiv2023

Voicify Your UI: Towards Android App Control with Voice Commands

Nowadays, voice assistants help users complete tasks on the smartphone with voice commands, replacing traditional touchscreen interactions when such interactions are inhibited. However, the usability of those tools remains moderate due to the problems in understanding rich language variations in human commands, along with efficiency and comprehensibility issues. Therefore, we introduce Voicify, an Android virtual assistant that allows users to interact with on-screen elements in mobile apps through voice commands. Using a novel deep learning command parser, Voicify interprets human verbal input and performs matching with UI elements. In addition, the tool can directly open a specific feature from installed applications by fetching application code information to explore the set of in-app components. Our command parser achieved 90\% accuracy on the human command dataset. Furthermore, the direct feature invocation module achieves better feature coverage in comparison to Google Assistant. The user study demonstrates the usefulness of Voicify in real-world scenarios.

cs.HC↗

arXiv2023

Efficiency Matters: Speeding Up Automated Testing with GUI Rendering Inference

Due to the importance of Android app quality assurance, many automated GUI testing tools have been developed. Although the test algorithms have been improved, the impact of GUI rendering has been overlooked. On the one hand, setting a long waiting time to execute events on fully rendered GUIs slows down the testing process. On the other hand, setting a short waiting time will cause the events to execute on partially rendered GUIs, which negatively affects the testing effectiveness. An optimal waiting time should strike a balance between effectiveness and efficiency. We propose AdaT, a lightweight image-based approach to dynamically adjust the inter-event time based on GUI rendering state. Given the real-time streaming on the GUI, AdaT presents a deep learning model to infer the rendering state, and synchronizes with the testing tool to schedule the next event when the GUI is fully rendered. The evaluations demonstrate the accuracy, efficiency, and effectiveness of our approach. We also integrate our approach with the existing automated testing tool to demonstrate the usefulness of AdaT in covering more activities and executing more events on fully rendered GUIs.

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arXiv2023

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph NAS methods mainly work under the homophily assumption and overlook another important graph property, i.e., heterophily, which exists widely in various real-world applications. To date, automated heterophilic graph learning with NAS is still a research blank to be filled in. Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. Specifically, Auto-HeG incorporates heterophily into all stages of automatic heterophilic graph learning, including search space design, supernet training, and architecture selection. Through the diverse message-passing scheme with joint micro-level and macro-level designs, we first build a comprehensive heterophilic GNN search space, enabling Auto-HeG to integrate complex and various heterophily of graphs. With a progressive supernet training strategy, we dynamically shrink the initial search space according to layer-wise variation of heterophily, resulting in a compact and efficient supernet. Taking a heterophily-aware distance criterion as the guidance, we conduct heterophilic architecture selection in the leave-one-out pattern, so that specialized and expressive heterophilic GNN architectures can be derived. Extensive experiments illustrate the superiority of Auto-HeG in developing excellent heterophilic GNNs to human-designed models and graph NAS models.

cs.LG↗

arXiv2023

Training-free Lexical Backdoor Attacks on Language Models

Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor attacks, which inject stealthy triggers into models for steering them to undesirable behaviors. Most existing backdoor attacks, such as data poisoning, require further (re)training or fine-tuning language models to learn the intended backdoor patterns. The additional training process however diminishes the stealthiness of the attacks, as training a language model usually requires long optimization time, a massive amount of data, and considerable modifications to the model parameters. In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models. Our attack is achieved by injecting lexical triggers into the tokenizer of a language model via manipulating its embedding dictionary using carefully designed rules. These rules are explainable to human developers which inspires attacks from a wider range of hackers. The sparse manipulation of the dictionary also habilitates the stealthiness of our attack. We conduct extensive experiments on three dominant NLP tasks based on nine language models to demonstrate the effectiveness and universality of our attack. The code of this work is available at https://github.com/Jinxhy/TFLexAttack.

cs.CR↗

arXiv2023

Read It, Don't Watch It: Captioning Bug Recordings Automatically

Screen recordings of mobile applications are easy to capture and include a wealth of information, making them a popular mechanism for users to inform developers of the problems encountered in the bug reports. However, watching the bug recordings and efficiently understanding the semantics of user actions can be time-consuming and tedious for developers. Inspired by the conception of the video subtitle in movie industry, we present a lightweight approach CAPdroid to caption bug recordings automatically. CAPdroid is a purely image-based and non-intrusive approach by using image processing and convolutional deep learning models to segment bug recordings, infer user action attributes, and generate subtitle descriptions. The automated experiments demonstrate the good performance of CAPdroid in inferring user actions from the recordings, and a user study confirms the usefulness of our generated step descriptions in assisting developers with bug replay.

cs.SE↗

arXiv2022

Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing

Automated GUI testing is widely used to help ensure the quality of mobile apps. However, many GUIs require appropriate text inputs to proceed to the next page which remains a prominent obstacle for testing coverage. Considering the diversity and semantic requirement of valid inputs (e.g., flight departure, movie name), it is challenging to automate the text input generation. Inspired by the fact that the pre-trained Large Language Model (LLM) has made outstanding progress in text generation, we propose an approach named QTypist based on LLM for intelligently generating semantic input text according to the GUI context. To boost the performance of LLM in the mobile testing scenario, we develop a prompt-based data construction and tuning method which automatically extracts the prompts and answers for model tuning. We evaluate QTypist on 106 apps from Google Play and the result shows that the passing rate of QTypist is 87%, which is 93% higher than the best baseline. We also integrate QTypist with the automated GUI testing tools and it can cover 42% more app activities, 52% more pages, and subsequently help reveal 122% more bugs compared with the raw tool.

cs.SE↗

arXiv2022

The Metamorphosis: Automatic Detection of Scaling Issues for Mobile Apps

As the bridge between users and software, Graphical User Interface (GUI) is critical to the app accessibility. Scaling up the font or display size of GUI can help improve the visual impact, readability, and usability of an app, and is frequently used by the elderly and people with vision impairment. Yet this can easily lead to scaling issues such as text truncation, component overlap, which negatively influence the acquirement of the right information and the fluent usage of the app. Previous techniques for UI display issue detection and cross-platform inconsistency detection cannot work well for these scaling issues. In this paper, we propose an automated method, dVermin, for scaling issue detection, through detecting the inconsistency of a view under the default and a larger display scale. The evaluation result shows that dVermin achieves 97% precision and 97% recall in issue page detection, and 84% precision and 91% recall for issue view detection, outperforming two state-of-the-art baselines by a large margin. We also evaluate dVermin with popular Android apps on F-droid, and successfully uncover 21 previously-undetected scaling issues with 20 of them being confirmed/fixed.

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arXiv2022

A Comparative Study of Smartphone and Smart TV Apps

Context: Smart TVs have become one of the most popular television types. Many app developers and service providers have designed TV versions for their smartphone applications. Despite the extensive studies on mobile app analysis, its TV equivalents receive far too little attention. The relationship between phone and TV has not been the subject of research works. Objective: In this paper, we aim to characterize the relationship between smartphone and smart TV apps. To fill this gap, we conduct a comparative study on smartphone and smart TV apps in this work, which is the starting and fundamental step to uncover the domain-specific challenges. Method: We gather a large-scale phone/TV app pairs from Google Play Store. We then analyzed the app pairs quantitatively and qualitatively from a variety of perspectives, including non-code (e.g., metadata, resources, permissions, etc.), code (e.g., components, methods, user interactions, etc.), security and privacy (e.g., reports of AndroBugs and FlowDroid). Results: Our experimental results indicate that (1) the code of the smartphone and TV apps can be released in the same app package or in separate app packages with the same package name; (2) 43% of resource files and 50% of code methods are reused between phone/TV app pairs; (3) TV and phone versions of the same app often encounter different kinds of security vulnerabilities; and (4) TV apps encounter fewer user interactions than their phone versions, but the type of user interaction events, surprisingly, are similar between phone/TV apps. Conclution: Our findings are valuable for developers and academics in comprehending the TV app ecosystem by providing additional insight into the migration of phone apps to TVs and the design mechanism of analysis tools for TV apps.

cs.SE↗

arXiv2022

An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users' Information Needs

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic. However, development often takes a long time and developers are not clear about many people's information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people's needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people's needs and make improvements to future crisis management dashboard development.

cs.HC↗

arXiv2022

Nighthawk: Fully Automated Localizing UI Display Issues via Visual Understanding

Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the upgrading of mobile devices and the development of aesthetics, the visual effects of the GUI are more and more attracting, and users pay more attention to the accessibility and usability of applications. However, such GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, component occlusion, missing image always occur during GUI rendering on different devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a fully automated approach, Nighthawk, based on deep learning for modelling visual information of the GUI screenshot. Nighthawk can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. At the same time, training the model needs a large amount of labeled buggy screenshots, which requires considerable manual effort to prepare them. We therefore propose a heuristic-based training data auto-generation method to automatically generate the labeled training data. The evaluation demonstrates that our Nighthawk can achieve average 0.84 precision and 0.84 recall in detecting UI display issues, average 0.59 AP and 0.60 AR in localizing these issues. We also evaluate Nighthawk with popular Android apps on Google Play and F-Droid, and successfully uncover 151 previously-undetected UI display issues with 75 of them being confirmed or fixed so far.

cs.SE↗

arXiv2022

NaviDroid: A Tool for Guiding Manual Android Testing via Hint Moves

Manual testing, as a complement to automated GUI testing, is the last line of defense for app quality especially in spotting usability and accessibility issues. However, the repeated actions and easy missing of some functionalities make manual testing time-consuming, labor-extensive and inefficient. Inspired by the game candy crush with flashy candies as hint moves for players, we develop a tool named NaviDroid for navigating human testers via highlighted next operations for more effective and efficient testing. Within NaviDroid, it constructs an enriched state transition graph (STG) with the trigger actions as the edges for two involved states. Based on the STG, NaviDroid utilizes the dynamic programming algorithm to plan the exploration path, and augment the run-time GUI with visualized hint moves for testers to quickly explore untested states and avoid duplication. The automated experiments demonstrate the high coverage and efficient path planning of NaviDroid. A user study further confirms its usefulness in the participants covering more states and activities, detecting more bugs within less time compared with the control group. NaviDroid demo video: https://youtu.be/lShFyg_nTA0.

cs.SE↗

arXiv2022

Smart App Attack: Hacking Deep Learning Models in Android Apps

On-device deep learning is rapidly gaining popularity in mobile applications. Compared to offloading deep learning from smartphones to the cloud, on-device deep learning enables offline model inference while preserving user privacy. However, such mechanisms inevitably store models on users' smartphones and may invite adversarial attacks as they are accessible to attackers. Due to the characteristic of the on-device model, most existing adversarial attacks cannot be directly applied for on-device models. In this paper, we introduce a grey-box adversarial attack framework to hack on-device models by crafting highly similar binary classification models based on identified transfer learning approaches and pre-trained models from TensorFlow Hub. We evaluate the attack effectiveness and generality in terms of four different settings including pre-trained models, datasets, transfer learning approaches and adversarial attack algorithms. The results demonstrate that the proposed attacks remain effective regardless of different settings, and significantly outperform state-of-the-art baselines. We further conduct an empirical study on real-world deep learning mobile apps collected from Google Play. Among 53 apps adopting transfer learning, we find that 71.7\% of them can be successfully attacked, which includes popular ones in medicine, automation, and finance categories with critical usage scenarios. The results call for the awareness and actions of deep learning mobile app developers to secure the on-device models. The code of this work is available at https://github.com/Jinxhy/SmartAppAttack

cs.LG↗

arXiv2022

GIFdroid: Automated Replay of Visual Bug Reports for Android Apps

Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using software. However, it is difficult for non-technical users to write clear descriptions about the bug occurrence. Therefore, more and more users begin to record the screen for reporting bugs as it is easy to be created and contains detailed procedures triggering the bug. But it is still tedious and time-consuming for developers to reproduce the bug due to the length and unclear actions within the recording. To overcome these issues, we propose GIFdroid, a light-weight approach to automatically replay the execution trace from visual bug reports. GIFdroid adopts image processing techniques to extract the keyframes from the recording, map them to states in GUI Transitions Graph, and generate the execution trace of those states to trigger the bug. Our automated experiments and user study demonstrate its accuracy, efficiency, and usefulness of the approach.

cs.SE↗

arXiv2022

Software Engineers Response to Public Crisis: Lessons Learnt from Spontaneously Building an Informative COVID-19 Dashboard

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant's in-person observations in which the authors acted as founders of https://covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future.

cs.SE↗

arXiv2022

Auto-Icon+: An Automated End-to-End Code Generation Tool for Icon Designs in UI Development

Approximately 50% of development resources are devoted to UI development tasks [9]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this paper, we present Auto-Icon+, an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.

cs.HC↗

arXiv2022

Gallery D.C.: Auto-created GUI Component Gallery for Design Search and Knowledge Discovery

GUI design is an integral part of software development. The process of designing a mobile application typically starts with the ideation and inspiration search from existing designs. However, existing information-retrieval based, and database-query based methods cannot efficiently gain inspirations in three requirements: design practicality, design granularity and design knowledge discovery. In this paper we propose a web application, called \tool that aims to facilitate the process of user interface design through real world GUI component search. Gallery D.C. indexes GUI component designs using reverse engineering and deep learning based computer vision techniques on millions of real world applications. To perform an advanced design search and knowledge discovery, our approach extracts information about size, color, component type, and text information to help designers explore multi-faceted design space and distill higher-order of design knowledge. Gallery D.C. is well received via an informal evaluation with 7 professional designers. Web Link: http://mui-collection.herokuapp.com/. Demo Video Link: https://youtu.be/zVmsz_wY5OQ.

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